1. Technical Field
The present invention generally relates to the field of digital data image processing, and more particularly, to a filter system and method for efficiently suppressing noise and improving the edge definition in a digitized image, while providing for extreme flexibility.
2. Related Art
The digital representation of an image through the use of an array of picture element (pixel) values is well known. For instance, medical imaging methods such as magnetic resonance imaging (MRI), ultrasound imaging, computerized tomography scanning (CT), and angiography, and non-medical methods such as radar imaging, all generate raw data which is converted into digitized image data through various mathematical transforms. The digitized image data is most often compiled in a two dimensional array of pixel values, for example, 256.times.256 pixels or 512.times.512 pixels in size, each pixel value typically represented by an 8-bit to 16-bit word. The size of the word reflects the precision of the image, e.g., the 16-bit word being more precise than the 8-bit word.
Inherent to all digitized images, regardless of the imaging methodology used, is the presence of noise which degrades the ideal image. The noise present within a digitized image is most often categorized as either high frequency, also known as speckle noise, or low frequency noise. The level of background high frequency noise in an image can affect the observers ability to detect a desired target feature. The detectability of an image feature is directly related to the contrast-to-noise ratio, defined as feature contrast relative to the surrounding image area divided by the noise content of the image. The level of local image contrast and noise content, which defines the contrast-to-noise ratio of the image, is determined in large part by user selectable parameters. For example, with magnetic resonance imaging (MRI), intrinsic tissue parameters, relaxation time T.sub.1, relaxation time T.sub.2, and proton density, in concert with user selectable image sequencing parameters, such as field-of-view, slice thickness, TE (echo time), and TR (recovery time) determine the noise content and contrast in an image, and therefore, have a direct bearing on the contrast to noise ratio. Therefore, because of the presence of noise, poor edge definition, and low target feature contrast relative to the background noise, the desired target features of a digitized image may not be distinguishable, and the usefulness of the image reduced.
In an effort to reduce the noise and increase edge definition of digitized images, many methods for modifying the image data of these digitized images have been developed. In general, the function of suppressing noise is achieved through the application of a low pass filter which performs a smoothing function on the image data. One such system designed to reduce the noise in a digitized image is disclosed in U.S. Pat. No. 4,783,753 to Crimmins. Conversely, improving edge definition is most often achieved through the application of a high pass filter which performs a sharpening function on the image data. A system designed to increase edge definition is disclosed in U.S. Pat. No. 5,038,388 to Song. Although the functions of reducing noise and sharpening edges are inherently contradictory, many image enhancement methods purport to perform both functions, as demonstrated by U.S. Pat. No. 5,271,064 to Dahwan et al. Additionally, some image enhancement methods purport to attain simultaneous noise reduction and increased edge definition. In this regard, see U.S. Pat. No. 5,031,227 to Raasch et al.
Implementation of any one of the aforementioned image enhancement methodologies is typically accomplished through a pixel-by-pixel analysis, utilizing the pixel value of those pixels within a predefined neighborhood of the pixel to be enhanced. The pixel value that is enhanced is typically increased or decreased as a result of applying several mathematical steps to the image data. The image enhancement methodologies are usually applied globally, whereby each pixel value of the image data is enhanced at least once, and often times more than once through iterative applications of a particular process. Some of the specific types of mathematical approaches taken in modifying digitized image data are a median filter, as disclosed in U.S. Pat. No. 4,736,439 to May, a hulling algorithm, as disclosed in U.S. Pat. No. 4,783,753 to Crimmins, an unsharp masking algorithm, as disclosed in U.S. Pat. No. 5,038,387 to Sakamoto, and an anisotropic filtering using a kernel comprising a matrix of coefficients, as disclosed in U.S. Pat. No. 5,003,618 to Meno.
Although successful to some extent, the methodologies developed thus far are generally complex and computationally intensive, and therefore, do not meet many of the requirements of the marketplace. First, the conventional methods require a great deal of processing time and power. A further disadvantage is the great expense associated with the processing apparatus, particularly the central processing unit. Secondly, in the medical community today, physicians who review the images require not only improved diagnostic capabilities but also an image with an aesthetic appearance which is realistic as well as accurate. However, many of the aforementioned methodologies either introduce artifacts into the image, which were not present in the original image, or remove or alter desired target features which were present in the original image. Lastly, many of the methodologies tend to connect discontinuous linear features within the image, and thereby potentially provide the basis for an inaccurate diagnosis.